AIM Score vs. Gene Expression
Full X range:
Auto X range:
Group Comparisons: Boxplots
CP73
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.269 | 0.610 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.658 |
| Model: | OLS | Adj. R-squared: | 0.604 |
| Method: | Least Squares | F-statistic: | 12.19 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.000112 |
| Time: | 04:03:59 | Log-Likelihood: | -100.76 |
| No. Observations: | 23 | AIC: | 209.5 |
| Df Residuals: | 19 | BIC: | 214.1 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -14.9516 | 103.942 | -0.144 | 0.887 | -232.505 202.602 |
| C(dose)[T.1] | 110.8025 | 115.595 | 0.959 | 0.350 | -131.141 352.746 |
| expression | 12.7412 | 19.116 | 0.667 | 0.513 | -27.268 52.751 |
| expression:C(dose)[T.1] | -10.4927 | 21.412 | -0.490 | 0.630 | -55.308 34.322 |
| Omnibus: | 0.875 | Durbin-Watson: | 1.779 |
| Prob(Omnibus): | 0.646 | Jarque-Bera (JB): | 0.734 |
| Skew: | 0.039 | Prob(JB): | 0.693 |
| Kurtosis: | 2.128 | Cond. No. | 212. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.654 |
| Model: | OLS | Adj. R-squared: | 0.619 |
| Method: | Least Squares | F-statistic: | 18.88 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 2.48e-05 |
| Time: | 04:03:59 | Log-Likelihood: | -100.91 |
| No. Observations: | 23 | AIC: | 207.8 |
| Df Residuals: | 20 | BIC: | 211.2 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 30.4439 | 46.243 | 0.658 | 0.518 | -66.017 126.904 |
| C(dose)[T.1] | 54.3308 | 8.920 | 6.091 | 0.000 | 35.724 72.938 |
| expression | 4.3781 | 8.447 | 0.518 | 0.610 | -13.241 21.997 |
| Omnibus: | 0.489 | Durbin-Watson: | 1.841 |
| Prob(Omnibus): | 0.783 | Jarque-Bera (JB): | 0.585 |
| Skew: | 0.120 | Prob(JB): | 0.746 |
| Kurtosis: | 2.256 | Cond. No. | 59.0 |
Model:
AIM ~ C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.649 |
| Model: | OLS | Adj. R-squared: | 0.632 |
| Method: | Least Squares | F-statistic: | 38.84 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 3.51e-06 |
| Time: | 04:03:59 | Log-Likelihood: | -101.06 |
| No. Observations: | 23 | AIC: | 206.1 |
| Df Residuals: | 21 | BIC: | 208.4 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 54.2083 | 5.919 | 9.159 | 0.000 | 41.900 66.517 |
| C(dose)[T.1] | 53.3371 | 8.558 | 6.232 | 0.000 | 35.539 71.135 |
| Omnibus: | 0.322 | Durbin-Watson: | 1.888 |
| Prob(Omnibus): | 0.851 | Jarque-Bera (JB): | 0.485 |
| Skew: | 0.060 | Prob(JB): | 0.785 |
| Kurtosis: | 2.299 | Cond. No. | 2.57 |
Model:
AIM ~ expression
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.011 |
| Model: | OLS | Adj. R-squared: | -0.036 |
| Method: | Least Squares | F-statistic: | 0.2411 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.628 |
| Time: | 04:03:59 | Log-Likelihood: | -112.97 |
| No. Observations: | 23 | AIC: | 229.9 |
| Df Residuals: | 21 | BIC: | 232.2 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 115.2488 | 72.713 | 1.585 | 0.128 | -35.966 266.464 |
| expression | -6.6795 | 13.602 | -0.491 | 0.628 | -34.967 21.608 |
| Omnibus: | 3.258 | Durbin-Watson: | 2.430 |
| Prob(Omnibus): | 0.196 | Jarque-Bera (JB): | 1.555 |
| Skew: | 0.286 | Prob(JB): | 0.460 |
| Kurtosis: | 1.862 | Cond. No. | 56.0 |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 5.845 | 0.032 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.701 |
| Model: | OLS | Adj. R-squared: | 0.620 |
| Method: | Least Squares | F-statistic: | 8.600 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.00318 |
| Time: | 04:03:59 | Log-Likelihood: | -66.243 |
| No. Observations: | 15 | AIC: | 140.5 |
| Df Residuals: | 11 | BIC: | 143.3 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -12.5172 | 112.199 | -0.112 | 0.913 | -259.465 234.431 |
| C(dose)[T.1] | -214.2631 | 161.548 | -1.326 | 0.212 | -569.828 141.302 |
| expression | 15.5315 | 21.730 | 0.715 | 0.490 | -32.295 63.358 |
| expression:C(dose)[T.1] | 50.6396 | 31.165 | 1.625 | 0.132 | -17.953 119.233 |
| Omnibus: | 0.258 | Durbin-Watson: | 1.379 |
| Prob(Omnibus): | 0.879 | Jarque-Bera (JB): | 0.101 |
| Skew: | -0.158 | Prob(JB): | 0.951 |
| Kurtosis: | 2.751 | Cond. No. | 190. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.629 |
| Model: | OLS | Adj. R-squared: | 0.568 |
| Method: | Least Squares | F-statistic: | 10.19 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.00259 |
| Time: | 04:03:59 | Log-Likelihood: | -67.857 |
| No. Observations: | 15 | AIC: | 141.7 |
| Df Residuals: | 12 | BIC: | 143.8 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -139.2399 | 85.999 | -1.619 | 0.131 | -326.616 48.136 |
| C(dose)[T.1] | 47.4968 | 12.926 | 3.674 | 0.003 | 19.333 75.660 |
| expression | 40.1507 | 16.607 | 2.418 | 0.032 | 3.967 76.334 |
| Omnibus: | 0.269 | Durbin-Watson: | 1.114 |
| Prob(Omnibus): | 0.874 | Jarque-Bera (JB): | 0.436 |
| Skew: | -0.168 | Prob(JB): | 0.804 |
| Kurtosis: | 2.235 | Cond. No. | 72.2 |
Model:
AIM ~ C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.449 |
| Model: | OLS | Adj. R-squared: | 0.406 |
| Method: | Least Squares | F-statistic: | 10.58 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.00629 |
| Time: | 04:03:59 | Log-Likelihood: | -70.833 |
| No. Observations: | 15 | AIC: | 145.7 |
| Df Residuals: | 13 | BIC: | 147.1 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 67.4286 | 11.044 | 6.106 | 0.000 | 43.570 91.287 |
| C(dose)[T.1] | 49.1964 | 15.122 | 3.253 | 0.006 | 16.527 81.866 |
| Omnibus: | 2.713 | Durbin-Watson: | 0.810 |
| Prob(Omnibus): | 0.258 | Jarque-Bera (JB): | 1.868 |
| Skew: | -0.843 | Prob(JB): | 0.393 |
| Kurtosis: | 2.619 | Cond. No. | 2.70 |
Model:
AIM ~ expression
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.212 |
| Model: | OLS | Adj. R-squared: | 0.152 |
| Method: | Least Squares | F-statistic: | 3.503 |
| Date: | Thu, 21 Nov 2024 | Prob (F-statistic): | 0.0839 |
| Time: | 04:03:59 | Log-Likelihood: | -73.511 |
| No. Observations: | 15 | AIC: | 151.0 |
| Df Residuals: | 13 | BIC: | 152.4 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -131.0660 | 120.410 | -1.088 | 0.296 | -391.196 129.064 |
| expression | 43.4695 | 23.225 | 1.872 | 0.084 | -6.706 93.644 |
| Omnibus: | 2.225 | Durbin-Watson: | 2.129 |
| Prob(Omnibus): | 0.329 | Jarque-Bera (JB): | 1.010 |
| Skew: | -0.112 | Prob(JB): | 0.604 |
| Kurtosis: | 1.749 | Cond. No. | 71.8 |